{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My methodology is as follows:\n",
    " 1. Clean the text using my [Tweet Cleaner](https://www.kaggle.com/jdparsons/tweet-cleaner) notebook\n",
    " 2. Send the clean text to GPT-2 using my [GPT-2: fake real disasters](https://www.kaggle.com/jdparsons/gpt-2-fake-real-disasters-data-augmentation) notebook. This generates similar tweets with the same label, which doubles the size of my training data.\n",
    " 3. In this notebook:\n",
    "  * I, um, use USE (Universal Sentence Encoder) to convert the tweets into 512 dimensional vectors\n",
    "  * Perform a grid search on many Light GBM parameters to find the best ones\n",
    "  * Use K-Fold Cross Validation to train multiple LGB models on different random splits of the data. The prediction function averages the votes into a final answer.\n",
    "\n",
    "My best score was 0.82413 with the previously mentioned preprocessing notebooks and the following Light GBM parameters:\n",
    " * K-fold DV=5\n",
    " * 'feature_fraction': 0.9, 'lambda_l1': 1.0, 'lambda_l2': 0.001, 'learning_rate': 0.05, 'max_depth': -1, 'n_estimators': 128, 'num_leaves': 64,\n",
    "\n",
    "My next experiment will be to send my GPT-2 augmented data to Google's AutoML and see how it scores compared to this hand-crafted approach. I'll publish that notebook and update this description when I have the results. **If you have any ideas for improving this notebook, please let me know in the comments!**\n",
    "\n",
    "Code linted via http://pep8online.com/ and https://yapf.now.sh/ to follow the Google python style guide (mostly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import auc, accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import GridSearchCV, KFold\n",
    "import tensorflow_hub as hub\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set pandas preview to use full width of browser to see more of the column data\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', -1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_files = False\n",
    "\n",
    "if show_files is True:\n",
    "    # helper method to quickly see the file paths of your imported data\n",
    "    for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "        for filename in filenames:\n",
    "            print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment these lines to try different types of preprocessed data\n",
    "\n",
    "#train_file_path = '../input/nlp-getting-started/train.csv'\n",
    "#train_file_path = '../input/tweet-cleaner/train_df_clean.csv'\n",
    "train_file_path = '../input/gpt-2-fake-real-disasters-data-augmentation/train_df_combined.csv'\n",
    "\n",
    "#test_file_path = '../input/nlp-getting-started/test.csv'\n",
    "test_file_path = '../input/tweet-cleaner/test_df_clean.csv'\n",
    "\n",
    "train_full = pd.read_csv(train_file_path)\n",
    "train = train_full[['text']]\n",
    "target = train_full[['target']]\n",
    "test = pd.read_csv(test_file_path)\n",
    "test = test[['id', 'text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://www.kaggle.com/denychaen/tweets-simple-baseline-tfembed-lgb\n",
    "embed = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/3\")\n",
    "X_train_embeddings = embed(train['text'].values)\n",
    "X_test_embeddings = embed(test['text'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_grid_search = False\n",
    "\n",
    "if do_grid_search is True:\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        np.array(X_train_embeddings['outputs']),\n",
    "        target,\n",
    "        test_size=0.2,\n",
    "        random_state=20)\n",
    "\n",
    "    estimator = lgb.LGBMClassifier()\n",
    "\n",
    "    # Try your own ranges here, you may find better ones than me!\n",
    "    param_grid = {\n",
    "        'n_estimators': [64, 200],\n",
    "        'num_leaves': [31, 64],\n",
    "        'learning_rate': [0.05, 0.1],\n",
    "        'feature_fraction': [0.8, 1.0],\n",
    "        'max_depth': [-1],\n",
    "        'lambda_l1': [0, 1],\n",
    "        'lambda_l2': [0, 1],\n",
    "    }\n",
    "\n",
    "    gridsearch = GridSearchCV(\n",
    "        estimator, param_grid, refit=True, verbose=0, cv=5)\n",
    "\n",
    "    gridsearch.fit(\n",
    "        X_train,\n",
    "        y_train,\n",
    "        eval_set=[(X_test, y_test)],\n",
    "        eval_metric=['auc', 'binary_logloss'],\n",
    "        early_stopping_rounds=10,\n",
    "        verbose=0)\n",
    "\n",
    "    print('Best parameters found by grid search are:', gridsearch.best_params_)\n",
    "\n",
    "    Y_pred = gridsearch.best_estimator_.predict(X_test)\n",
    "\n",
    "    print(metrics.classification_report(y_test, Y_pred, digits=3),)\n",
    "    print(metrics.confusion_matrix(y_test, Y_pred))\n",
    "\n",
    "    # if we just guessed the most common class (0) for every prediction\n",
    "    print('The null acccuracy is:',\n",
    "          max(y_test['target'].mean(), 1 - y_test['target'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 5 rounds\n",
      "[50]\ttraining's binary_logloss: 0.259718\tvalid_1's binary_logloss: 0.434387\n",
      "Early stopping, best iteration is:\n",
      "[91]\ttraining's binary_logloss: 0.159325\tvalid_1's binary_logloss: 0.413877\n",
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 5 rounds\n",
      "[50]\ttraining's binary_logloss: 0.254325\tvalid_1's binary_logloss: 0.469768\n",
      "Early stopping, best iteration is:\n",
      "[83]\ttraining's binary_logloss: 0.168796\tvalid_1's binary_logloss: 0.457989\n",
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 5 rounds\n",
      "[50]\ttraining's binary_logloss: 0.249448\tvalid_1's binary_logloss: 0.492717\n",
      "Early stopping, best iteration is:\n",
      "[92]\ttraining's binary_logloss: 0.147278\tvalid_1's binary_logloss: 0.473451\n",
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 5 rounds\n",
      "[50]\ttraining's binary_logloss: 0.25664\tvalid_1's binary_logloss: 0.468467\n",
      "[100]\ttraining's binary_logloss: 0.141426\tvalid_1's binary_logloss: 0.445671\n",
      "Early stopping, best iteration is:\n",
      "[95]\ttraining's binary_logloss: 0.149093\tvalid_1's binary_logloss: 0.445554\n",
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 5 rounds\n",
      "[50]\ttraining's binary_logloss: 0.259638\tvalid_1's binary_logloss: 0.43877\n",
      "[100]\ttraining's binary_logloss: 0.145094\tvalid_1's binary_logloss: 0.412474\n",
      "Early stopping, best iteration is:\n",
      "[104]\ttraining's binary_logloss: 0.139445\tvalid_1's binary_logloss: 0.4112\n",
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {\n",
    "    'objective': 'binary',\n",
    "    'feature_fraction': 0.9,\n",
    "    'lambda_l1': 1.0,\n",
    "    'lambda_l2': 0.001,\n",
    "    'learning_rate': 0.05,\n",
    "    'max_depth': -1,\n",
    "    'n_estimators': 128,\n",
    "    'num_leaves': 64,\n",
    "    'metric': 'binary_logloss',\n",
    "    'random_seed': 42\n",
    "}\n",
    "\n",
    "kf = KFold(n_splits=5, random_state=42)\n",
    "models = []\n",
    "\n",
    "X_train = pd.DataFrame(data=np.array(X_train_embeddings['outputs']))\n",
    "y_train = target\n",
    "\n",
    "for train_index, test_index in kf.split(X_train):\n",
    "    train_features = X_train.loc[train_index]\n",
    "    train_target = y_train.loc[train_index]\n",
    "\n",
    "    test_features = X_train.loc[test_index]\n",
    "    test_target = y_train.loc[test_index]\n",
    "\n",
    "    d_training = lgb.Dataset(\n",
    "        train_features, label=train_target, free_raw_data=False)\n",
    "    d_test = lgb.Dataset(test_features, label=test_target, free_raw_data=False)\n",
    "\n",
    "    evals_result = {}  # to record eval results for plotting\n",
    "\n",
    "    model = lgb.train(\n",
    "        params,\n",
    "        train_set=d_training,\n",
    "        num_boost_round=200,\n",
    "        valid_sets=[d_training, d_test],\n",
    "        verbose_eval=50,\n",
    "        early_stopping_rounds=5,\n",
    "        evals_result=evals_result)\n",
    "\n",
    "    # https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/\n",
    "    print('Plotting metrics recorded during training...')\n",
    "    ax = lgb.plot_metric(evals_result, metric='binary_logloss')\n",
    "    plt.show()\n",
    "\n",
    "    models.append(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/rohanrao/ashrae-half-and-half\n",
    "# averages the votes of each model, then returns the majority vote\n",
    "def get_predictions(data):\n",
    "\n",
    "    results = []\n",
    "    for model in models:\n",
    "        if results == []:\n",
    "            results = model.predict(\n",
    "                data, num_iteration=model.best_iteration) / len(models)\n",
    "        else:\n",
    "            results += model.predict(\n",
    "                data, num_iteration=model.best_iteration) / len(models)\n",
    "\n",
    "    # if the average value is less than .5, then the majority vote was 0, otherwise it was 1\n",
    "    results = [int(round(x)) for x in results]\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/lightgbm/basic.py:546: UserWarning: Converting data to scipy sparse matrix.\n",
      "  warnings.warn('Converting data to scipy sparse matrix.')\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "preds = get_predictions(X_test_embeddings['outputs'])\n",
    "\n",
    "ssub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')\n",
    "\n",
    "ssub[\"target\"] = preds\n",
    "ssub.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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